Ember Alerts

Assessing Wireless Emergency Alert Messages in Wildfires Using the Warning Response Model

Datos Bibliográficos

ID21738142
AutoresErica D Kuligowski (0000-0001-6121-4983, RMIT University, autor de correspondencia), Nicholas Waugh (0009-0006-0945-721X, Albany State University), Nicholas A Waugh (Research Associate, College of Emergency Preparedness, Homeland Security and Cybersecurity, Univ. at Albany, State Univ. of New York, 1220 Washington Ave., Albany, NY 12226), Jeannette Sutton (0000-0002-4345-9108, Albany State University), Thomas J Cova (0000-0002-4136-5140, University of Utah)
Año2023
Volumen24
Número2
Fecha de publicación2023-05-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaNatural Hazards Review (JOURNAL)
Identificadores de la revistaISSN: 1527-6988 • E-ISSN: 1527-6996
EditorialAmerican Society of Civil Engineers (ASCE) (PUBLISHER • US)
DOI10.1061/nhrefo.nheng-1724
OpenAlexW4317938021
IdiomaEN
Citas recibidas21
Referencias citadas34

When evacuation is necessary in a wildfire event, affected communities must be alerted and warned of the imminent danger and instructed on what to do to protect themselves. One channel available to message providers in the United States is Wireless Emergency Alerts (WEAs) disseminated via the Integrated Public Alert and Warning System (IPAWS). Recent wildfire events have shed light on the need to improve WEA strategies and messages when alerting exposed populations of imminent fire threat. The purpose of this article is to assess how, when, and where WEAs have been used in US wildfires; whether they comply with guidance set out by Mileti and Sorensen’s Warning Response Model (WRM); and whether the expansion in characters (from 90 to 360) of WEA messages has influenced compliance with the WRM. A quantitative content analysis was conducted of WEA messages sent during US wildfires from January 2020–April 2022. A total of 1,284 messages were manually coded based upon the content and style categories identified in the WRM. Descriptive analyses (and Chi-square tests) were performed to illustrate how 90-character and 360-character WEA messages differ by key content and style features. Results showed that certain content features (i.e., location, guidance, and the name of the hazard) were included more often than others (i.e., source, hazard description, hazard consequences, and timing information) and that the inclusion of most content features increased with increasing message character length. Additionally, when assessing message ‘completeness,’ the use of acronyms was prevalent in both 90- and 360-character wildfire WEAs; whereas the inclusion of URLs was linked to increased message length. Wildfire WEAs also displayed inconsistency both within and across states in their use of terminology to trigger evacuation. These findings, among others, have implications for theory highlighting a growing need to confirm that message receivers understand and can act on the messages sent, regardless of the language used. In addition, for message creators, recommendations for effective WEA messages for wildfires are provided

Computer security · Content analysis · Hazard · Medical emergency · Sociology · Telecommunications · Warning system · Computer Science · Disaster Management and Resilience · Evacuation and Crowd Dynamics · Fire effects on ecosystems · Medicine